Autoencoders are a family of unsupervised learning methods that use neural network architectures and learning algorithms to learn a lower-dimensional representation (encoding) of the data, which can then be used to reconstruct a representation close to the original input. They thus facilitate dimensionality reduction for prediction and classification [1]}, [2]}, and have been successfully applied to image classification [3]}, [4]}, face recognition [5]}, [6]}, geoscience and remote sensing [7]}, speech-based emotion recognition [8]}, and data generation [9]}. Autoencoders have been prominent in deep neural network architectures and also for transfer learning tasks [10]}, [11]}. They are an active research area, with recent developments including regularized autoencoders [12]}, variational autoencoders [13]}, [2]}, [15]}, [16]}, [17]}, adversarial autoencoders [18]}, [19]}, [8]}, [21]}, [22]}, variational graph autoencoders [23]}, [24]}, and convolutional autoencoders [10]}.
